I always encounter the term "ETL" when reading a job post for a data engineer role. Eventually, I googled it and finally found its meaning - Extract, Transform, Load. It's an automated process of extracting data from different sources, transforming it into a usable, unified format, and then loading it all together in a destination environment where it can be easily analyzed and used for decision-making. It's an essential process in making sense of large amounts of data and turning it into valuable insights.
I went deep dive into the context and along the way, I found out that it's the traditional way of data pipeline engineering methodology. The contemporary way of it? It's "ELT". And yes, it still has the same abbreviation meaning but obviously, L and T are interchanged. For ELT processes, data is extracted, and then loaded as-is, into its destination environment. From there, users can transform the data however the users wish. So, what's the deal on why ELT exists?
First, we have to understand the pain points of the traditional way - ETL:
Information loss can happen during the transformation process.
Change request in the transformation process is dependent on the IT team to deliver the change, which can lead to lengthy time-to-insight.
When handling big data, there is a tendency to overspend on on-premise computing resources, which resources can be underutilized resulting in inefficiency.
ELT emerged as a solution, which was enabled mainly due to available cloud platform technologies.
No loss of information as the data extracted is loaded 'as is' to the destination environment, where it will be transformed.
End-users can do their transformations at will in the destination environment, without needing IT to do it.
Cloud computing solutions can practically handle big data, can scale on demand and promotes cost efficiency.
Key Differences between ETL and ELT
To summarize:
| Difference | ETL | ELT |
| When and where do the transformations happen | within the data pipeline | happens in the destination environment |
| Flexibility | rigid - pipelines are engineered according to user specifications | end-users build their own data transformations |
| Handling big data | - On-premise computing - Scaling is difficult - Handles structured data | - Cloud computing - Flexible - Can handle both structured and unstructured data |
| Time-to-insight | - Heavily dependent with IT teams/ developer to complete the pipeline | - Supports self-service, ad hoc, interactive analytics in real time |
Overall, the main difference is the order in which the transformations take place. ETL performs data transformations before loading the data into the target system, while ELT loads the data first and then performs transformations within the target system. ELT is often associated with modern cloud-based data platforms that can handle massive volumes of raw data and perform transformations efficiently at scale. ETL, on the other hand, is a more traditional approach that may be suitable for smaller datasets or when significant data processing is required before loading.